{"id":"W4410706113","doi":"10.12927/hcq.2025.27579","title":"Developing Personas to Enable Tailored Public Health Communications: The Case of Organ Donation in Québec","year":2025,"lang":"en","type":"article","venue":"Healthcare Quarterly","topic":"Persona Design and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère de l'Agriculture, des Pêcheries et de l'Alimentation; Natural Sciences and Engineering Research Council of Canada","funders":"","keywords":"Organ donation; Context (archaeology); Public health; Persona; Triangulation; Medicine; Focus group; Public relations; Donation; Nursing; Business; Transplantation; Political science; Surgery; Computer science; Law; Marketing; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03122622,0.0005486808,0.0003696464,0.001325163,0.006894664,0.003696757,0.001601887,0.001604724,0.003787558],"category_scores_gemma":[0.03930908,0.0003805277,0.0005110742,0.001729129,0.002689441,0.002287062,0.003821122,0.001179204,0.000550194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.015644,"about_ca_system_score_gemma":0.02120245,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4059064,"about_ca_topic_score_gemma":0.5644641,"domain_scores_codex":[0.9724823,0.02454029,0.0004687335,0.0007612414,0.0007565487,0.0009908899],"domain_scores_gemma":[0.9598275,0.02693683,0.001613718,0.00227719,0.007279329,0.002065553],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.001259624,0.002074186,0.09127285,0.001914853,0.0002116849,0.002181837,0.4308464,0.01053194,0.008656884,0.04877212,0.01827848,0.3839991],"study_design_scores_gemma":[0.0009281626,0.002857661,0.0776507,0.002343418,0.0004092834,0.0006038068,0.5358463,0.06989048,0.007753066,0.02876421,0.2724381,0.0005148013],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7674983,0.0005869927,0.183639,0.01184835,0.0001757702,0.007742536,0.001113928,0.000787435,0.02660766],"genre_scores_gemma":[0.7405592,0.0002377193,0.2479729,0.001173011,0.00001171511,0.004413768,0.0005310437,0.00006572933,0.005034978],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5940936,"threshold_uncertainty_score":0.8070875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0781138749430381,"score_gpt":0.3485138462071677,"score_spread":0.2703999712641296,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}